实时直接分析技术(direct analysis in real-time,DART)是一种在大气压环境下,利用非表面接触型热解析,对化合物进行离子化的新型软电离技术。该技术通过与样品预处理技术、质谱技术相结合,为直接、快速、无损地现场原位分析提供了一种...实时直接分析技术(direct analysis in real-time,DART)是一种在大气压环境下,利用非表面接触型热解析,对化合物进行离子化的新型软电离技术。该技术通过与样品预处理技术、质谱技术相结合,为直接、快速、无损地现场原位分析提供了一种新的途径,在农兽药残留、违禁添加成分、接触材料迁移物、生物毒素的检测方面应用广泛。该文简述实时直接分析技术的电离机理,概述该方法在常见的食品污染物检测中的应用现状和局限性,并对其未来发展方向进行展望。展开更多
Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
文摘实时直接分析技术(direct analysis in real-time,DART)是一种在大气压环境下,利用非表面接触型热解析,对化合物进行离子化的新型软电离技术。该技术通过与样品预处理技术、质谱技术相结合,为直接、快速、无损地现场原位分析提供了一种新的途径,在农兽药残留、违禁添加成分、接触材料迁移物、生物毒素的检测方面应用广泛。该文简述实时直接分析技术的电离机理,概述该方法在常见的食品污染物检测中的应用现状和局限性,并对其未来发展方向进行展望。
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
